Overview

Brought to you by YData

Dataset statistics

Number of variables 15
Number of observations 891
Missing cells 0
Missing cells (%) 0.0%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 104.5 KiB
Average record size in memory 120.1 B

Variable types

Numeric 6
Categorical 7
Text 2

Alerts

FamilySize is highly overall correlated with Fare and 3 other fields High correlation
Fare is highly overall correlated with FamilySize and 1 other fields High correlation
Has_Cabin is highly overall correlated with Fare and 1 other fields High correlation
IsAlone is highly overall correlated with FamilySize and 2 other fields High correlation
Parch is highly overall correlated with FamilySize and 1 other fields High correlation
Pclass is highly overall correlated with Has_Cabin High correlation
Sex is highly overall correlated with Survived and 1 other fields High correlation
SibSp is highly overall correlated with FamilySize and 1 other fields High correlation
Survived is highly overall correlated with Sex and 1 other fields High correlation
Title is highly overall correlated with Sex and 1 other fields High correlation
PassengerId is uniformly distributed Uniform
PassengerId has unique values Unique
Name has unique values Unique
SibSp has 608 (68.2%) zeros Zeros
Parch has 678 (76.1%) zeros Zeros
Fare has 15 (1.7%) zeros Zeros

Reproduction

Analysis started 2025-09-24 13:40:25.715493
Analysis finished 2025-09-24 13:40:28.804720
Duration 3.09 seconds
Software version ydata-profiling vv4.17.0
Download configuration config.json

Variables

PassengerId
Real number (ℝ)

Uniform  Unique 

Distinct 891
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 446
Minimum 1
Maximum 891
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 7.1 KiB
2025-09-24T19:10:28.855796 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 45.5
Q1 223.5
median 446
Q3 668.5
95-th percentile 846.5
Maximum 891
Range 890
Interquartile range (IQR) 445

Descriptive statistics

Standard deviation 257.35384
Coefficient of variation (CV) 0.57702655
Kurtosis -1.2
Mean 446
Median Absolute Deviation (MAD) 223
Skewness 0
Sum 397386
Variance 66231
Monotonicity Strictly increasing
2025-09-24T19:10:28.924645 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
891 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
Other values (881) 881
98.9%
Value Count Frequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
Value Count Frequency (%)
891 1
0.1%
890 1
0.1%
889 1
0.1%
888 1
0.1%
887 1
0.1%
886 1
0.1%
885 1
0.1%
884 1
0.1%
883 1
0.1%
882 1
0.1%

Survived
Categorical

High correlation 

Distinct 2
Distinct (%) 0.2%
Missing 0
Missing (%) 0.0%
Memory size 7.1 KiB
0
549 
1
342 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 891
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 1
3rd row 1
4th row 1
5th row 0

Common Values

Value Count Frequency (%)
0 549
61.6%
1 342
38.4%

Length

2025-09-24T19:10:28.992533 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-24T19:10:29.015207 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
0 549
61.6%
1 342
38.4%

Most occurring characters

Value Count Frequency (%)
0 549
61.6%
1 342
38.4%

Most occurring categories

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 549
61.6%
1 342
38.4%

Most occurring scripts

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 549
61.6%
1 342
38.4%

Most occurring blocks

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 549
61.6%
1 342
38.4%

Pclass
Categorical

High correlation 

Distinct 3
Distinct (%) 0.3%
Missing 0
Missing (%) 0.0%
Memory size 7.1 KiB
3
491 
1
216 
2
184 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 891
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 3
2nd row 1
3rd row 3
4th row 1
5th row 3

Common Values

Value Count Frequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Length

2025-09-24T19:10:29.066692 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-24T19:10:29.095622 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring characters

Value Count Frequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Name
Text

Unique 

Distinct 891
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Memory size 7.1 KiB
2025-09-24T19:10:29.209800 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Length

Max length 82
Median length 52
Mean length 26.965208
Min length 12

Characters and Unicode

Total characters 24026
Distinct characters 60
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 891 ?
Unique (%) 100.0%

Sample

1st row Braund, Mr. Owen Harris
2nd row Cumings, Mrs. John Bradley (Florence Briggs Thayer)
3rd row Heikkinen, Miss. Laina
4th row Futrelle, Mrs. Jacques Heath (Lily May Peel)
5th row Allen, Mr. William Henry
Value Count Frequency (%)
mr 521
 
14.4%
miss 182
 
5.0%
mrs 129
 
3.6%
william 64
 
1.8%
john 44
 
1.2%
master 40
 
1.1%
henry 35
 
1.0%
james 24
 
0.7%
george 24
 
0.7%
charles 23
 
0.6%
Other values (1515) 2538
70.0%
2025-09-24T19:10:29.538018 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring categories

Value Count Frequency (%)
(unknown) 24026
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring scripts

Value Count Frequency (%)
(unknown) 24026
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring blocks

Value Count Frequency (%)
(unknown) 24026
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Sex
Categorical

High correlation 

Distinct 2
Distinct (%) 0.2%
Missing 0
Missing (%) 0.0%
Memory size 7.1 KiB
male
577 
female
314 

Length

Max length 6
Median length 4
Mean length 4.704826
Min length 4

Characters and Unicode

Total characters 4192
Distinct characters 5
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row male
2nd row female
3rd row female
4th row female
5th row male

Common Values

Value Count Frequency (%)
male 577
64.8%
female 314
35.2%

Length

2025-09-24T19:10:29.593603 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-24T19:10:29.641211 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
male 577
64.8%
female 314
35.2%

Most occurring characters

Value Count Frequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 4192
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 4192
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 4192
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Age
Real number (ℝ)

Distinct 88
Distinct (%) 9.9%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 29.361582
Minimum 0.42
Maximum 80
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 7.1 KiB
2025-09-24T19:10:29.697789 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0.42
5-th percentile 6
Q1 22
median 28
Q3 35
95-th percentile 54
Maximum 80
Range 79.58
Interquartile range (IQR) 13

Descriptive statistics

Standard deviation 13.019697
Coefficient of variation (CV) 0.44342625
Kurtosis 0.99387102
Mean 29.361582
Median Absolute Deviation (MAD) 6
Skewness 0.51024466
Sum 26161.17
Variance 169.5125
Monotonicity Not monotonic
2025-09-24T19:10:29.779173 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
28 202
22.7%
24 30
 
3.4%
22 27
 
3.0%
18 26
 
2.9%
30 25
 
2.8%
19 25
 
2.8%
21 24
 
2.7%
25 23
 
2.6%
36 22
 
2.5%
29 20
 
2.2%
Other values (78) 467
52.4%
Value Count Frequency (%)
0.42 1
 
0.1%
0.67 1
 
0.1%
0.75 2
 
0.2%
0.83 2
 
0.2%
0.92 1
 
0.1%
1 7
0.8%
2 10
1.1%
3 6
0.7%
4 10
1.1%
5 4
 
0.4%
Value Count Frequency (%)
80 1
 
0.1%
74 1
 
0.1%
71 2
0.2%
70.5 1
 
0.1%
70 2
0.2%
66 1
 
0.1%
65 3
0.3%
64 2
0.2%
63 2
0.2%
62 4
0.4%

SibSp
Real number (ℝ)

High correlation  Zeros 

Distinct 7
Distinct (%) 0.8%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.52300786
Minimum 0
Maximum 8
Zeros 608
Zeros (%) 68.2%
Negative 0
Negative (%) 0.0%
Memory size 7.1 KiB
2025-09-24T19:10:29.832298 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 0
median 0
Q3 1
95-th percentile 3
Maximum 8
Range 8
Interquartile range (IQR) 1

Descriptive statistics

Standard deviation 1.1027434
Coefficient of variation (CV) 2.1084644
Kurtosis 17.88042
Mean 0.52300786
Median Absolute Deviation (MAD) 0
Skewness 3.6953517
Sum 466
Variance 1.2160431
Monotonicity Not monotonic
2025-09-24T19:10:29.888017 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
Value Count Frequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
4 18
 
2.0%
3 16
 
1.8%
8 7
 
0.8%
5 5
 
0.6%
Value Count Frequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
3 16
 
1.8%
4 18
 
2.0%
5 5
 
0.6%
8 7
 
0.8%
Value Count Frequency (%)
8 7
 
0.8%
5 5
 
0.6%
4 18
 
2.0%
3 16
 
1.8%
2 28
 
3.1%
1 209
 
23.5%
0 608
68.2%

Parch
Real number (ℝ)

High correlation  Zeros 

Distinct 7
Distinct (%) 0.8%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.38159371
Minimum 0
Maximum 6
Zeros 678
Zeros (%) 76.1%
Negative 0
Negative (%) 0.0%
Memory size 7.1 KiB
2025-09-24T19:10:29.936972 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 0
median 0
Q3 0
95-th percentile 2
Maximum 6
Range 6
Interquartile range (IQR) 0

Descriptive statistics

Standard deviation 0.80605722
Coefficient of variation (CV) 2.1123441
Kurtosis 9.7781252
Mean 0.38159371
Median Absolute Deviation (MAD) 0
Skewness 2.749117
Sum 340
Variance 0.64972824
Monotonicity Not monotonic
2025-09-24T19:10:29.987318 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
Value Count Frequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
5 5
 
0.6%
3 5
 
0.6%
4 4
 
0.4%
6 1
 
0.1%
Value Count Frequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
3 5
 
0.6%
4 4
 
0.4%
5 5
 
0.6%
6 1
 
0.1%
Value Count Frequency (%)
6 1
 
0.1%
5 5
 
0.6%
4 4
 
0.4%
3 5
 
0.6%
2 80
 
9.0%
1 118
 
13.2%
0 678
76.1%

Ticket
Text

Distinct 681
Distinct (%) 76.4%
Missing 0
Missing (%) 0.0%
Memory size 7.1 KiB
2025-09-24T19:10:30.130795 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Length

Max length 18
Median length 17
Mean length 6.7508418
Min length 3

Characters and Unicode

Total characters 6015
Distinct characters 35
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 547 ?
Unique (%) 61.4%

Sample

1st row A/5 21171
2nd row PC 17599
3rd row STON/O2. 3101282
4th row 113803
5th row 373450
Value Count Frequency (%)
pc 60
 
5.3%
c.a 27
 
2.4%
a/5 17
 
1.5%
ca 14
 
1.2%
ston/o 12
 
1.1%
2 12
 
1.1%
w./c 9
 
0.8%
sc/paris 9
 
0.8%
soton/o.q 8
 
0.7%
soton/oq 7
 
0.6%
Other values (709) 955
84.5%
2025-09-24T19:10:30.348102 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 6015
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 6015
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 6015
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Fare
Real number (ℝ)

High correlation  Zeros 

Distinct 248
Distinct (%) 27.8%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 32.204208
Minimum 0
Maximum 512.3292
Zeros 15
Zeros (%) 1.7%
Negative 0
Negative (%) 0.0%
Memory size 7.1 KiB
2025-09-24T19:10:30.411068 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 7.225
Q1 7.9104
median 14.4542
Q3 31
95-th percentile 112.07915
Maximum 512.3292
Range 512.3292
Interquartile range (IQR) 23.0896

Descriptive statistics

Standard deviation 49.693429
Coefficient of variation (CV) 1.5430725
Kurtosis 33.398141
Mean 32.204208
Median Absolute Deviation (MAD) 6.9042
Skewness 4.7873165
Sum 28693.949
Variance 2469.4368
Monotonicity Not monotonic
2025-09-24T19:10:30.478239 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
8.05 43
 
4.8%
13 42
 
4.7%
7.8958 38
 
4.3%
7.75 34
 
3.8%
26 31
 
3.5%
10.5 24
 
2.7%
7.925 18
 
2.0%
7.775 16
 
1.8%
7.2292 15
 
1.7%
26.55 15
 
1.7%
Other values (238) 615
69.0%
Value Count Frequency (%)
0 15
1.7%
4.0125 1
 
0.1%
5 1
 
0.1%
6.2375 1
 
0.1%
6.4375 1
 
0.1%
6.45 1
 
0.1%
6.4958 2
 
0.2%
6.75 2
 
0.2%
6.8583 1
 
0.1%
6.95 1
 
0.1%
Value Count Frequency (%)
512.3292 3
0.3%
263 4
0.4%
262.375 2
0.2%
247.5208 2
0.2%
227.525 4
0.4%
221.7792 1
 
0.1%
211.5 1
 
0.1%
211.3375 3
0.3%
164.8667 2
0.2%
153.4625 3
0.3%

Embarked
Categorical

Distinct 3
Distinct (%) 0.3%
Missing 0
Missing (%) 0.0%
Memory size 7.1 KiB
S
646 
C
168 
Q
77 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 891
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row S
2nd row C
3rd row S
4th row S
5th row S

Common Values

Value Count Frequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Length

2025-09-24T19:10:30.548664 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-24T19:10:30.583223 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
s 646
72.5%
c 168
 
18.9%
q 77
 
8.6%

Most occurring characters

Value Count Frequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Most occurring categories

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Most occurring scripts

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Most occurring blocks

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Has_Cabin
Categorical

High correlation 

Distinct 2
Distinct (%) 0.2%
Missing 0
Missing (%) 0.0%
Memory size 7.1 KiB
0
687 
1
204 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 891
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 1
3rd row 0
4th row 1
5th row 0

Common Values

Value Count Frequency (%)
0 687
77.1%
1 204
 
22.9%

Length

2025-09-24T19:10:30.635047 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-24T19:10:30.669652 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
0 687
77.1%
1 204
 
22.9%

Most occurring characters

Value Count Frequency (%)
0 687
77.1%
1 204
 
22.9%

Most occurring categories

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 687
77.1%
1 204
 
22.9%

Most occurring scripts

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 687
77.1%
1 204
 
22.9%

Most occurring blocks

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 687
77.1%
1 204
 
22.9%

FamilySize
Real number (ℝ)

High correlation 

Distinct 9
Distinct (%) 1.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 1.9046016
Minimum 1
Maximum 11
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 7.1 KiB
2025-09-24T19:10:30.698572 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 1
Q1 1
median 1
Q3 2
95-th percentile 6
Maximum 11
Range 10
Interquartile range (IQR) 1

Descriptive statistics

Standard deviation 1.6134585
Coefficient of variation (CV) 0.84713704
Kurtosis 9.159666
Mean 1.9046016
Median Absolute Deviation (MAD) 0
Skewness 2.7274415
Sum 1697
Variance 2.6032485
Monotonicity Not monotonic
2025-09-24T19:10:30.750038 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
Value Count Frequency (%)
1 537
60.3%
2 161
 
18.1%
3 102
 
11.4%
4 29
 
3.3%
6 22
 
2.5%
5 15
 
1.7%
7 12
 
1.3%
11 7
 
0.8%
8 6
 
0.7%
Value Count Frequency (%)
1 537
60.3%
2 161
 
18.1%
3 102
 
11.4%
4 29
 
3.3%
5 15
 
1.7%
6 22
 
2.5%
7 12
 
1.3%
8 6
 
0.7%
11 7
 
0.8%
Value Count Frequency (%)
11 7
 
0.8%
8 6
 
0.7%
7 12
 
1.3%
6 22
 
2.5%
5 15
 
1.7%
4 29
 
3.3%
3 102
 
11.4%
2 161
 
18.1%
1 537
60.3%

IsAlone
Categorical

High correlation 

Distinct 2
Distinct (%) 0.2%
Missing 0
Missing (%) 0.0%
Memory size 7.1 KiB
1
537 
0
354 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 891
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0
2nd row 0
3rd row 1
4th row 0
5th row 1

Common Values

Value Count Frequency (%)
1 537
60.3%
0 354
39.7%

Length

2025-09-24T19:10:30.798621 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-24T19:10:30.847277 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
1 537
60.3%
0 354
39.7%

Most occurring characters

Value Count Frequency (%)
1 537
60.3%
0 354
39.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
1 537
60.3%
0 354
39.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
1 537
60.3%
0 354
39.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
1 537
60.3%
0 354
39.7%

Title
Categorical

High correlation 

Distinct 5
Distinct (%) 0.6%
Missing 0
Missing (%) 0.0%
Memory size 7.1 KiB
Mr
517 
Miss
185 
Mrs
126 
Master
 
40
Rare
 
23

Length

Max length 6
Median length 2
Mean length 2.7878788
Min length 2

Characters and Unicode

Total characters 2484
Distinct characters 8
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Mr
2nd row Mrs
3rd row Miss
4th row Mrs
5th row Mr

Common Values

Value Count Frequency (%)
Mr 517
58.0%
Miss 185
 
20.8%
Mrs 126
 
14.1%
Master 40
 
4.5%
Rare 23
 
2.6%

Length

2025-09-24T19:10:30.895665 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-24T19:10:30.951398 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
mr 517
58.0%
miss 185
 
20.8%
mrs 126
 
14.1%
master 40
 
4.5%
rare 23
 
2.6%

Most occurring characters

Value Count Frequency (%)
M 868
34.9%
r 706
28.4%
s 536
21.6%
i 185
 
7.4%
a 63
 
2.5%
e 63
 
2.5%
t 40
 
1.6%
R 23
 
0.9%

Most occurring categories

Value Count Frequency (%)
(unknown) 2484
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
M 868
34.9%
r 706
28.4%
s 536
21.6%
i 185
 
7.4%
a 63
 
2.5%
e 63
 
2.5%
t 40
 
1.6%
R 23
 
0.9%

Most occurring scripts

Value Count Frequency (%)
(unknown) 2484
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
M 868
34.9%
r 706
28.4%
s 536
21.6%
i 185
 
7.4%
a 63
 
2.5%
e 63
 
2.5%
t 40
 
1.6%
R 23
 
0.9%

Most occurring blocks

Value Count Frequency (%)
(unknown) 2484
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
M 868
34.9%
r 706
28.4%
s 536
21.6%
i 185
 
7.4%
a 63
 
2.5%
e 63
 
2.5%
t 40
 
1.6%
R 23
 
0.9%

Interactions

2025-09-24T19:10:28.279150 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:26.162377 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:26.508592 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:26.890800 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:27.532080 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:27.904307 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:28.335212 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:26.209711 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:26.580885 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:26.961875 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:27.591769 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:27.957830 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:28.397234 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:26.273687 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:26.637733 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:27.030371 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:27.664789 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:28.021383 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:28.458459 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:26.342312 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:26.714529 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:27.357210 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:27.724977 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:28.104777 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:28.516407 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:26.397410 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:26.777383 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:27.412382 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:27.779207 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:28.149527 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:28.574701 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:26.460104 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:26.836966 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:27.466527 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:27.848369 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-09-24T19:10:28.221382 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-24T19:10:31.015608 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Age Embarked FamilySize Fare Has_Cabin IsAlone Parch PassengerId Pclass Sex SibSp Survived Title
Age 1.000 0.151 -0.183 0.126 0.278 0.348 -0.217 0.035 0.265 0.106 -0.145 0.158 0.365
Embarked 0.151 1.000 0.083 0.195 0.226 0.110 0.052 0.000 0.258 0.111 0.092 0.164 0.130
FamilySize -0.183 0.083 1.000 0.529 0.070 0.642 0.801 -0.050 0.137 0.205 0.849 0.215 0.252
Fare 0.126 0.195 0.529 1.000 0.582 0.304 0.410 -0.014 0.479 0.189 0.447 0.283 0.097
Has_Cabin 0.278 0.226 0.070 0.582 1.000 0.152 0.091 0.063 0.790 0.134 0.138 0.313 0.158
IsAlone 0.348 0.110 0.642 0.304 0.152 1.000 0.686 0.000 0.127 0.300 0.837 0.198 0.495
Parch -0.217 0.052 0.801 0.410 0.091 0.686 1.000 0.001 0.022 0.247 0.450 0.157 0.269
PassengerId 0.035 0.000 -0.050 -0.014 0.063 0.000 0.001 1.000 0.032 0.066 -0.061 0.104 0.040
Pclass 0.265 0.258 0.137 0.479 0.790 0.127 0.022 0.032 1.000 0.130 0.148 0.337 0.189
Sex 0.106 0.111 0.205 0.189 0.134 0.300 0.247 0.066 0.130 1.000 0.206 0.540 0.992
SibSp -0.145 0.092 0.849 0.447 0.138 0.837 0.450 -0.061 0.148 0.206 1.000 0.187 0.294
Survived 0.158 0.164 0.215 0.283 0.313 0.198 0.157 0.104 0.337 0.540 0.187 1.000 0.565
Title 0.365 0.130 0.252 0.097 0.158 0.495 0.269 0.040 0.189 0.992 0.294 0.565 1.000

Missing values

2025-09-24T19:10:28.673265 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-24T19:10:28.755262 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Embarked Has_Cabin FamilySize IsAlone Title
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 S 0 2 0 Mr
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.0 1 0 PC 17599 71.2833 C 1 2 0 Mrs
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 S 0 1 1 Miss
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 S 1 2 0 Mrs
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 S 0 1 1 Mr
5 6 0 3 Moran, Mr. James male 28.0 0 0 330877 8.4583 Q 0 1 1 Mr
6 7 0 1 McCarthy, Mr. Timothy J male 54.0 0 0 17463 51.8625 S 1 1 1 Mr
7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 3 1 349909 21.0750 S 0 5 0 Master
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 2 347742 11.1333 S 0 3 0 Mrs
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 0 237736 30.0708 C 0 2 0 Mrs
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Embarked Has_Cabin FamilySize IsAlone Title
881 882 0 3 Markun, Mr. Johann male 33.0 0 0 349257 7.8958 S 0 1 1 Mr
882 883 0 3 Dahlberg, Miss. Gerda Ulrika female 22.0 0 0 7552 10.5167 S 0 1 1 Miss
883 884 0 2 Banfield, Mr. Frederick James male 28.0 0 0 C.A./SOTON 34068 10.5000 S 0 1 1 Mr
884 885 0 3 Sutehall, Mr. Henry Jr male 25.0 0 0 SOTON/OQ 392076 7.0500 S 0 1 1 Mr
885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 Q 0 6 0 Mrs
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 S 0 1 1 Rare
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.0000 S 1 1 1 Miss
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female 28.0 1 2 W./C. 6607 23.4500 S 0 4 0 Miss
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C 1 1 1 Mr
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 Q 0 1 1 Mr